Building LLM Agent Systems with AutoGen Training Course

Introduction to LLM Agent Systems

• Concepts of LLM agents and multi-agent architectures
• Overview of the AutoGen framework and its ecosystem
• Agent roles: user proxy, assistant, function caller, and others

Setup and Configuration of AutoGen

• Establishing the Python environment and dependencies
• Foundations of AutoGen configuration files
• Integration with LLM providers (OpenAI, Azure, local models)

Agent Design and Role Allocation

• Analysis of agent types and conversational patterns
• Definition of agent objectives, prompts, and directives
• Task delegation and control flow based on roles

Function Calling and Tool Integration

• Registration of functions for agent utilization
• Autonomous and collaborative function execution
• Linking external APIs and Python scripts to agents

Conversation Management and Memory Handling

• Session tracking and persistent memory management
• Agent-to-agent messaging and token processing
• Oversight of conversation context and historical data

End-to-End Agent Workflows

• Construction of multi-step collaborative tasks (e.g., document analysis, code review)
• Simulation of user-agent dialogues and decision chains
• Debugging and optimization of agent performance

Use Cases and Production Deployment

• Internal automation agents for research, reporting, and scripting
• External-facing bots such as chat assistants and voice integrations
• Packaging and deploying agent systems in production environments

Summary and Future Directions